OpenRaiser/Intern-Atlas huggingface dataset:
High Speed Event Camera TRacking
Venue: arXiv cs.CV 2020
Year: 2020

Event cameras are bioinspired sensors with reaction times in the order of microseconds. This property makes them appealing for use in highly-dynamic computer vision applications. In this work,we explore the limits of this sensing technology and present an ultra-fast tracking algorithm able to estimate six-degree-of-freedom motion with dynamics over 25.8 g, at a throughput of 10 kHz,processing over a million events per second. Our method is capable of tracking either camera motion or the motion of an object in front of it, using an error-state Kalman filter formulated in a Lie-theoretic sense. The method includes a robust mechanism for the matching of events with projected line segments with very fast outlier rejection. Meticulous treatment of sparse matrices is applied to achieve real-time performance. Different motion models of varying complexity are considered for the sake of comparison and performance analysis
Compressing Deep Convolutional Neural Networks by Stacking Low-dimensional Binary Convolution Filters
Venue: arXiv cs.CV 2020
Year: 2020

Deep Convolutional Neural Networks (CNN) have been successfully applied to many real-life problems. However, the huge memory cost of deep CNN models poses a great challenge of deploying them on memory-constrained devices (e.g., mobile phones). One popular way to reduce the memory cost of deep CNN model is to train binary CNN where the weights in convolution filters are either 1 or -1 and therefore each weight can be efficiently stored using a single bit. However, the compression ratio of existing binary CNN models is upper bounded by around 32. To address this limitation, we propose a novel method to compress deep CNN model by stacking low-dimensional binary convolution filters. Our proposed method approximates a standard convolution filter by selecting and stacking filters from a set of low-dimensional binary convolution filters. This set of low-dimensional binary convolution filters is shared across all filters for a given convolution layer. Therefore, our method will achieve much larger compression ratio than binary CNN models. In order to train our proposed model, we have theoretically shown that our proposed model is equivalent to select and stack intermediate feature maps generated by low-dimensional binary filters. Therefore, our proposed model can be efficiently trained using the split-transform-merge strategy. We also provide detailed analysis of the memory and computation cost of our model in model inference. We compared the proposed method with other five popular model compression techniques on two benchmark datasets. Our experimental results have demonstrated that our proposed method achieves much higher compression ratio than existing methods while maintains comparable accuracy.
Spectral clustering of annotated graphs using a factor graph representation
Venue: arXiv cs.SI 2020
Year: 2020

Graph-structured data commonly have node annotations. A popular approach for inference and learning involving annotated graphs is to incorporate annotations into a statistical model or algorithm. By contrast, we consider a more direct method named scotch-taping, in which the structural information in a graph and its node annotations are encoded as a factor graph. Specifically, we establish the mathematical basis of this method in the spectral framework.
QADiscourse -- Discourse Relations as QA Pairs: Representation, Crowdsourcing and Baselines
Venue: arXiv cs.CL 2020
Year: 2020

Discourse relations describe how two propositions relate to one another, and identifying them automatically is an integral part of natural language understanding. However, annotating discourse relations typically requires expert annotators. Recently, different semantic aspects of a sentence have been represented and crowd-sourced via question-and-answer (QA) pairs. This paper proposes a novel representation of discourse relations as QA pairs, which in turn allows us to crowd-source wide-coverage data annotated with discourse relations, via an intuitively appealing interface for composing such questions and answers. Based on our proposed representation, we collect a novel and wide-coverage QADiscourse dataset, and present baseline algorithms for predicting QADiscourse relations.
WoLFRaM: Enhancing Wear-Leveling and Fault Tolerance in Resistive Memories using Programmable Address Decoders
Venue: arXiv cs.AR 2020
Year: 2020

Resistive memories have limited lifetime caused by limited write endurance and highly non-uniform write access patterns. Two main techniques to mitigate endurance-related memory failures are 1) wear-leveling, to evenly distribute the writes across the entire memory, and 2) fault tolerance, to correct memory cell failures. However, one of the main open challenges in extending the lifetime of existing resistive memories is to make both techniques work together seamlessly and efficiently. To address this challenge, we propose WoLFRaM, a new mechanism that combines both wear-leveling and fault tolerance techniques at low cost by using a programmable resistive address decoder (PRAD). The key idea of WoLFRaM is to use PRAD for implementing 1) a new efficient wear-leveling mechanism that remaps write accesses to random physical locations on the fly, and 2) a new efficient fault tolerance mechanism that recovers from faults by remapping failed memory blocks to available physical locations. Our evaluations show that, for a Phase Change Memory (PCM) based system with cell endurance of 108 writes, WoLFRaM increases the memory lifetime by 68% compared to a baseline that implements the best state-of-the-art wear-leveling and fault correction mechanisms. WoLFRaM's average / worst-case performance and energy overheads are 0.51% / 3.8% and 0.47% / 2.1% respectively.
Robustness and Reliability of Gender Bias Assessment in Word Embeddings: The Role of Base Pairs
Venue: arXiv cs.CL 2020
Year: 2020

It has been shown that word embeddings can exhibit gender bias, and various methods have been proposed to quantify this. However, the extent to which the methods are capturing social stereotypes inherited from the data has been debated. Bias is a complex concept and there exist multiple ways to define it. Previous work has leveraged gender word pairs to measure bias and extract biased analogies. We show that the reliance on these gendered pairs has strong limitations: bias measures based off of them are not robust and cannot identify common types of real-world bias, whilst analogies utilising them are unsuitable indicators of bias. In particular, the well-known analogy "man is to computer-programmer as woman is to homemaker" is due to word similarity rather than societal bias. This has important implications for work on measuring bias in embeddings and related work debiasing embeddings.
Balanced incomplete block designs and exact satisfiability
Venue: arXiv cs.CC 2020
Year: 2020

The paper explores the correspondence between balanced incomplete block designs (BIBD) and certain linear CNF formulas by identifying the points of a block design with the clauses of the Boolean formula and blocks with Boolean variables. Parallel classes in BIBDs correspond to XSAT solutions in the corresponding formula. This correspondence allows for transfers of results from one field to the other. As a new result we deduce from known satisfiability theorems that the problem of finding a parallel class in a partially balanced incomplete block design is NP-complete.
Using Sentences as Semantic Representations in Large Scale Zero-Shot Learning
Venue: arXiv cs.CV 2020
Year: 2020

Zero-shot learning aims to recognize instances of unseen classes, for which no visual instance is available during training, by learning multimodal relations between samples from seen classes and corresponding class semantic representations. These class representations usually consist of either attributes, which do not scale well to large datasets, or word embeddings, which lead to poorer performance. A good trade-off could be to employ short sentences in natural language as class descriptions. We explore different solutions to use such short descriptions in a ZSL setting and show that while simple methods cannot achieve very good results with sentences alone, a combination of usual word embeddings and sentences can significantly outperform current state-of-the-art.
GRETA: Graph-based Real-time Event Trend Aggregation
Venue: arXiv cs.DS 2020
Year: 2020

Streaming applications from algorithmic trading to traffic management deploy Kleene patterns to detect and aggregate arbitrarily-long event sequences, called event trends. State-of-the-art systems process such queries in two steps. Namely, they first construct all trends and then aggregate them. Due to the exponential costs of trend construction, this two-step approach suffers from both a long delays and high memory costs. To overcome these limitations, we propose the Graph-based Real-time Event Trend Aggregation (Greta) approach that dynamically computes event trend aggregation without first constructing these trends. We define the Greta graph to compactly encode all trends. Our Greta runtime incrementally maintains the graph, while dynamically propagating aggregates along its edges. Based on the graph, the final aggregate is incrementally updated and instantaneously returned at the end of each query window. Our Greta runtime represents a win-win solution, reducing both the time complexity from exponential to quadratic and the space complexity from exponential to linear in the number of events. Our experiments demonstrate that Greta achieves up to four orders of magnitude speed-up and up to 50--fold memory reduction compared to the state-of-the-art two-step approaches.
Online Action Detection in Streaming Videos with Time Buffers
Venue: arXiv cs.CV 2020
Year: 2020

We formulate the problem of online temporal action detection in live streaming videos, acknowledging one important property of live streaming videos that there is normally a broadcast delay between the latest captured frame and the actual frame viewed by the audience. The standard setting of the online action detection task requires immediate prediction after a new frame is captured. We illustrate that its lack of consideration of the delay is imposing unnecessary constraints on the models and thus not suitable for this problem. We propose to adopt the problem setting that allows models to make use of the small `buffer time' incurred by the delay in live streaming videos. We design an action start and end detection framework for this online with buffer setting with two major components: flattened I3D and window-based suppression. Experiments on three standard temporal action detection benchmarks under the proposed setting demonstrate the effectiveness of the proposed framework. We show that by having a suitable problem setting for this problem with wide-applications, we can achieve much better detection accuracy than off-the-shelf online action detection models.
Global Self-Attention Networks for Image Recognition
Venue: arXiv cs.CV 2020
Year: 2020

Recently, a series of works in computer vision have shown promising results on various image and video understanding tasks using self-attention. However, due to the quadratic computational and memory complexities of self-attention, these works either apply attention only to low-resolution feature maps in later stages of a deep network or restrict the receptive field of attention in each layer to a small local region. To overcome these limitations, this work introduces a new global self-attention module, referred to as the GSA module, which is efficient enough to serve as the backbone component of a deep network. This module consists of two parallel layers: a content attention layer that attends to pixels based only on their content and a positional attention layer that attends to pixels based on their spatial locations. The output of this module is the sum of the outputs of the two layers. Based on the proposed GSA module, we introduce new standalone global attention-based deep networks that use GSA modules instead of convolutions to model pixel interactions. Due to the global extent of the proposed GSA module, a GSA network has the ability to model long-range pixel interactions throughout the network. Our experimental results show that GSA networks outperform the corresponding convolution-based networks significantly on the CIFAR-100 and ImageNet datasets while using less parameters and computations. The proposed GSA networks also outperform various existing attention-based networks on the ImageNet dataset.
A deep learning pipeline for identification of motor units in musculoskeletal ultrasound
Venue: arXiv cs.CV 2020
Year: 2020

Ultrasound imaging provides information from a large part of the muscle. It has recently been shown that ultrafast ultrasound imaging can be used to record and analyze the mechanical response of individual MUs using blind source separation. In this work, we present an alternative method - a deep learning pipeline - to identify active MUs in ultrasound image sequences, including segmentation of their territories and signal estimation of their mechanical responses (twitch train). We train and evaluate the model using simulated data mimicking the complex activation pattern of tens of activated MUs with overlapping territories and partially synchronized activation patterns. Using a slow fusion approach (based on 3D CNNs), we transform the spatiotemporal image sequence data to 2D representations and apply a deep neural network architecture for segmentation. Next, we employ a second deep neural network architecture for signal estimation. The results show that the proposed pipeline can effectively identify individual MUs, estimate their territories, and estimate their twitch train signal at low contraction forces. The framework can retain spatio-temporal consistencies and information of the mechanical response of MU activity even when the ultrasound image sequences are transformed into a 2D representation for compatibility with more traditional computer vision and image processing techniques. The proposed pipeline is potentially useful to identify simultaneously active MUs in whole muscles in ultrasound image sequences of voluntary skeletal muscle contractions at low force levels.
Rotate to Attend: Convolutional Triplet Attention Module
Venue: arXiv cs.CV 2020
Year: 2020

Benefiting from the capability of building inter-dependencies among channels or spatial locations, attention mechanisms have been extensively studied and broadly used in a variety of computer vision tasks recently. In this paper, we investigate light-weight but effective attention mechanisms and present triplet attention, a novel method for computing attention weights by capturing cross-dimension interaction using a three-branch structure. For an input tensor, triplet attention builds inter-dimensional dependencies by the rotation operation followed by residual transformations and encodes inter-channel and spatial information with negligible computational overhead. Our method is simple as well as efficient and can be easily plugged into classic backbone networks as an add-on module. We demonstrate the effectiveness of our method on various challenging tasks including image classification on ImageNet-1k and object detection on MSCOCO and PASCAL VOC datasets. Furthermore, we provide extensive in-sight into the performance of triplet attention by visually inspecting the GradCAM and GradCAM++ results. The empirical evaluation of our method supports our intuition on the importance of capturing dependencies across dimensions when computing attention weights. Code for this paper can be publicly accessed at https://github.com/LandskapeAI/triplet-attention
DiPair: Fast and Accurate Distillation for Trillion-Scale Text Matching and Pair Modeling
Venue: arXiv cs.CL 2020
Year: 2020

Pre-trained models like BERT (Devlin et al., 2018) have dominated NLP / IR applications such as single sentence classification, text pair classification, and question answering. However, deploying these models in real systems is highly non-trivial due to their exorbitant computational costs. A common remedy to this is knowledge distillation (Hinton et al., 2015), leading to faster inference. However -- as we show here -- existing works are not optimized for dealing with pairs (or tuples) of texts. Consequently, they are either not scalable or demonstrate subpar performance. In this work, we propose DiPair -- a novel framework for distilling fast and accurate models on text pair tasks. Coupled with an end-to-end training strategy, DiPair is both highly scalable and offers improved quality-speed tradeoffs. Empirical studies conducted on both academic and real-world e-commerce benchmarks demonstrate the efficacy of the proposed approach with speedups of over 350x and minimal quality drop relative to the cross-attention teacher BERT model.
Instance-Dependent Complexity of Contextual Bandits and Reinforcement Learning: A Disagreement-Based Perspective
Venue: arXiv cs.LG 2020
Year: 2020

In the classical multi-armed bandit problem, instance-dependent algorithms attain improved performance on "easy" problems with a gap between the best and second-best arm. Are similar guarantees possible for contextual bandits? While positive results are known for certain special cases, there is no general theory characterizing when and how instance-dependent regret bounds for contextual bandits can be achieved for rich, general classes of policies. We introduce a family of complexity measures that are both sufficient and necessary to obtain instance-dependent regret bounds. We then introduce new oracle-efficient algorithms which adapt to the gap whenever possible, while also attaining the minimax rate in the worst case. Finally, we provide structural results that tie together a number of complexity measures previously proposed throughout contextual bandits, reinforcement learning, and active learning and elucidate their role in determining the optimal instance-dependent regret. In a large-scale empirical evaluation, we find that our approach often gives superior results for challenging exploration problems. Turning our focus to reinforcement learning with function approximation, we develop new oracle-efficient algorithms for reinforcement learning with rich observations that obtain optimal gap-dependent sample complexity.
Conditional Generative Modeling via Learning the Latent Space
Venue: arXiv cs.LG 2020
Year: 2020

Although deep learning has achieved appealing results on several machine learning tasks, most of the models are deterministic at inference, limiting their application to single-modal settings. We propose a novel general-purpose framework for conditional generation in multimodal spaces, that uses latent variables to model generalizable learning patterns while minimizing a family of regression cost functions. At inference, the latent variables are optimized to find optimal solutions corresponding to multiple output modes. Compared to existing generative solutions, in multimodal spaces, our approach demonstrates faster and stable convergence, and can learn better representations for downstream tasks. Importantly, it provides a simple generic model that can beat highly engineered pipelines tailored using domain expertise on a variety of tasks, while generating diverse outputs. Our codes will be released.
SDC Resilient Error-bounded Lossy Compressor
Venue: arXiv cs.DC 2020
Year: 2020

Lossy compression is one of the most important strategies to resolve the big science data issue, however, little work was done to make it resilient against silent data corruptions (SDC). In fact, SDC is becoming non-negligible because of exa-scale computing demand on complex scientific simulations with vast volume of data being produced or in some particular instruments/devices (such as interplanetary space probe) that need to transfer large amount of data in an error-prone environment. In this paper, we propose an SDC resilient error-bounded lossy compressor upon the SZ compression framework. Specifically, we adopt a new independent-block-wise model that decomposes the entire dataset into many independent sub-blocks to compress. Then, we design and implement a series of error detection/correction strategies based on SZ. We are the first to extend algorithm-based fault tolerance (ABFT) to lossy compression. Our proposed solution incurs negligible execution overhead without soft errors. It keeps the correctness of decompressed data still bounded within user's requirement with a very limited degradation of compression ratios upon soft errors.
OpenIE6: Iterative Grid Labeling and Coordination Analysis for Open Information Extraction
Venue: arXiv cs.CL 2020
Year: 2020

A recent state-of-the-art neural open information extraction (OpenIE) system generates extractions iteratively, requiring repeated encoding of partial outputs. This comes at a significant computational cost. On the other hand, sequence labeling approaches for OpenIE are much faster, but worse in extraction quality. In this paper, we bridge this trade-off by presenting an iterative labeling-based system that establishes a new state of the art for OpenIE, while extracting 10x faster. This is achieved through a novel Iterative Grid Labeling (IGL) architecture, which treats OpenIE as a 2-D grid labeling task. We improve its performance further by applying coverage (soft) constraints on the grid at training time. Moreover, on observing that the best OpenIE systems falter at handling coordination structures, our OpenIE system also incorporates a new coordination analyzer built with the same IGL architecture. This IGL based coordination analyzer helps our OpenIE system handle complicated coordination structures, while also establishing a new state of the art on the task of coordination analysis, with a 12.3 pts improvement in F1 over previous analyzers. Our OpenIE system, OpenIE6, beats the previous systems by as much as 4 pts in F1, while being much faster.
The construction and weight distributions of all projective binary linear codes
Venue: arXiv cs.IT 2020
Year: 2020

Boolean functions can be used to construct binary linear codes in many ways, and vice versa. The objective of this short article is to point out a connection between the weight distributions of all projective binary linear codes and the Walsh spectra of all Boolean functions. New research problems are also proposed.
Theedhum Nandrum@Dravidian-CodeMix-FIRE2020: A Sentiment Polarity Classifier for YouTube Comments with Code-switching between Tamil, Malayalam and English
Venue: arXiv cs.CL 2020
Year: 2020

Theedhum Nandrum is a sentiment polarity detection system using two approaches--a Stochastic Gradient Descent (SGD) based classifier and a Long Short-term Memory (LSTM) based Classifier. Our approach utilises language features like use of emoji, choice of scripts and code mixing which appeared quite marked in the datasets specified for the Dravidian Codemix - FIRE 2020 task. The hyperparameters for the SGD were tuned using GridSearchCV. Our system was ranked 4th in Tamil-English with a weighted average F1 score of 0.62 and 9th in Malayalam-English with a score of 0.65. We achieved a weighted average F1 score of 0.77 for Tamil-English using a Logistic Regression based model after the task deadline. This performance betters the top ranked classifier on this dataset by a wide margin. Our use of language-specific Soundex to harmonise the spelling variants in code-mixed data appears to be a novel application of Soundex. Our complete code is published in github at https://github.com/oligoglot/theedhum-nandrum.